Reliability of Tabular Generative Model Evaluation Benchmarks Under Structural Complexity and Mixed Data Types

Generative models have revolutionized multiple domains, yet their application to tabular data remains underexplored. Evaluating generative models for tabular data presents unique challenges due to structural complexity, large-scale variability, and mixed data types, making it difficult to intuitively capture intricate patterns. Existing evaluation metrics offer only partial insights, lacking a comprehensive measure of generative performance. To address this limitation, we propose three novel evaluation metrics: FAED, FPCAD, and RFIS. Our extensive experimental analysis, conducted on three stan

Research goal: To what extent do structural complexity and mixed data types in tabular generative models impact the reliability of current evaluation benchmarks compared to simple statistical metrics?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.0/10.